Drs. Shaalan Beg and Travis Osterman discuss a machine learning model, recently featured in JCO Clinical Cancer Informatics, that uses electronic health record data to accurately predict the effectiveness and toxicity of treatment with immune checkpoint inhibitors. The new AI model can be used to provide a personalized risk-benefit profile, inform therapeutic decision-making, and improve clinical trial cohort selection.   TRANSCRIPT Dr. Shaalan Beg: Hello, and welcome to the ASCO Daily News Podcast. I'm Dr. Shaalan Beg, your guest host for today. I am an adjunct associate professor at UT Southwestern's Simmons Comprehensive Cancer Center.  Cancer immunotherapy has transformed the treatment landscape by providing new and effective treatment options for many solid and hematologic malignancies. But while many patients experience a remarkable response to immune checkpoint inhibitors, other patients can suffer life-threatening immune checkpoint toxicities. Today, we will be discussing a machine learning solution that can assess a patient's immune checkpoint inhibitor risk-benefit profile based primarily on routinely collected structured electronic health record data. This novel AI solution was recently featured in JCO Clinical Cancer Informatics, and I am delighted to welcome one of the report's authors, Dr. Travis Osterman. He is an associate vice president for research informatics and associate professor in the Department of Biomedical Informatics and the Division of Hematology Oncology at Vanderbilt University Medical Center in Nashville, Tennessee. Dr. Osterman also serves as the director of cancer clinical informatics at the Vanderbilt Ingram Cancer Center.  Our full disclosures are available in the transcript of this episode, and disclosures related to all episodes of the podcast are available at asco.org/DNpod.  Dr. Osterman, it's great to have you on the podcast today.   Dr. Travis Osterman: Thanks, Shaalan. It's great to be here. Thank you for the invitation.   Dr. Shaalan Beg: Congratulations on your recently published article in the JCO CCI titled "Prediction of Effectiveness and Toxicities of Immune Checkpoint Inhibitors Using Real World Patient Data." Why did you decide to address this specific problem?   Dr. Travis Osterman: I am a practicing medical oncologist at Vanderbilt, I specialize in thoracic malignancies. Immunotherapy has been a significant part of my practice from the beginning. And I think for all of us, we have patients in our practices that are tremendous responders. I have stories of my patients, a few of which, at least, are able to get years of benefit even after stopping therapy, and potentially some even stage 4 patients that are amazingly seemingly cured after their treatments. But I also have patients that experience severe toxicities, some of those are life-threatening or life ending, but many of those carry morbidity. In my population, I see a lot of pneumonitis, and that really alters patients' quality of life. And the biggest conversation I have with patients is: “How do I know which of these outcomes I’m going to have, if I’m going to get benefits from these therapies or am I going to get one of these side effects or toxicities?” And we set out to try to answer that question with data.   Dr. Shaalan Beg: When electronic medical records started to make their way into the clinic, I remember all of us thinking about the wonderful applications where we could use the data to help guide the clinical care, assign the right treatment for the right patient at the right time, and learn from other patients' experiences to improve the care of the person who’s in front of us. And my personal opinion is that we haven’t realized our electronic medical records’ potential to that extent. And efforts like the one you published in JCO CCI is the culmination of one of the efforts, and I can only imagine how much time and effort it must have taken to develop that and we’re hoping is the first of many more to